Method for authenticating identity of handset user

ABSTRACT

A method for authenticating the identity of a handset user is provided. The method includes: obtaining, a login account and a password from the user; judging whether the login account and the password are correct; if the login account or the password is incorrect, refusing the user to access an operating system of the handset; if the login account and the password are correct, sending the login account and the password to a cloud server, wherein the login account and the password correspond to a face sample image library of the user stored on the cloud server; acquiring an input face image of the user; sending the input face image to the cloud server; authenticating, by the cloud server, the identity of the user according to the login account, the password and the input face image.

CROSS REFFERENCE TO RELATED APPLICATIONS

This application is a continuation of International ApplicationNo.PCTCN2011/076623, filed on Jun. 30, 2011, which is herebyincorporated by reference in its entirety.

FIELD OF THE INVENTION

The present invention relates to the field of communicationtechnologies, and in particular, to a method for authenticating theidentity of a handset user.

BACKGROUND OF THE INVENTION

With the widespread popularity of handsets, especially smart handsets,the security of the operating systems of the handsets becomesincreasingly important. Currently, most smart handsets only use a loginaccount and a password of a user as a means for identity authentication.However, this method is not secure, and as soon as the login account andthe password are stolen by other users, all data on the operating systemof the handset is exposed.

The technology using biological features of a human body, especially theface, for security authentication, develops rapidly. However, thecomputational complexity of the face authentication is high, and thecomputational resources of a handset are generally limited, so that itis difficult to support the face authentication with a heavycomputational burden. In addition, in conventional face authenticationsystems, face authentication algorithms are rough and thus theprobability of misjudgment is very high.

SUMMARY OF THE INVENTION

Embodiments of the present invention provide a method for authenticatingthe identity of a handset user. Embodiments of the present invention arebased on the cloud computing technology, the cloud server can bear theload of identity authentication, thus improving the security of theoperating system of the handset, enhancing the user's experience andraising the precision of the face authentication.

An embodiment of the present invention provides a method forauthenticating the identity of a handset user, wherein the user'shandset is connected to the cloud server through a communicationnetwork, the cloud server stores a face sample image librarycorresponding to the user, and the method includes:

-   -   obtaining, by the handset, a login account and a password from        the user;    -   judging, by the handset, whether the login account and the        password are correct;    -   if the login account or the password is incorrect, refusing, by        the handset, the user to access an operating system of the        handset;    -   if the login account and the password are correct, sending, by        the handset, the login account and the password to the cloud        server, wherein the login account and the password correspond to        a face sample image library of the user stored on the cloud        server;    -   acquiring, by the handset, an input face image of the user;    -   sending, by the handset, the input face image to the cloud        server;    -   authenticating, by the cloud server, the identity of the user        according to the login account, the password and the input face        image;    -   judging, by the cloud server, whether the user is allowed to        access the operating system of the handset;    -   wherein the step of judging whether the user is allowed to        access the operating system of the handset comprises:    -   step A. determining, by the cloud server, according to the login        account and the password, the face sample image library of the        user corresponding to the login account and the password;    -   step B. obtaining, by the cloud server, a face feature        similarity value (FFSV) according to the input face image and        the face sample image library; wherein the face feature        similarity value represents the degree of similarity between the        input face image and each face sample image in the face sample        image library;    -   wherein the step of B comprises:    -   step B1. obtaining, by the cloud server, a face region image        from the input face image;    -   step B2. calculating, by the cloud server, a first        characteristic value of each face sample image in the face        sample image library and a second characteristic value of the        face region image;    -   step B3. calculating, by the cloud server, a characteristic        distance between the first characteristic value of each face        sample image in the face sample image library and the second        characteristic value of the face region image, to obtain        multiple second characteristic distances; determining, by the        cloud server, a face feature similarity value (FFSV) according        to the multiple second characteristic distances;    -   step C. judging, the cloud server, whether the face feature        similarity value is larger than a preset threshold, wherein the        preset threshold is obtained according to multiple first        characteristic distances between each face sample image in the        face sample image library;    -   step D. if the face feature similarity value is not larger than        the preset threshold, allowing, by the cloud server, the user to        access the operating system of the handset;    -   step E. if the face feature similarity value is larger than the        preset threshold, calculating, by the cloud server, a first        number and a second number, wherein the first number is the        number of face sample images in the face sample image library        corresponding to the first characteristic distances which are        larger than the face feature similarity value, and the second        number is the number of face sample images in the face sample        image library corresponding to the first characteristic        distances which are not larger than the face feature similarity        value; judging, by the cloud server, whether the first number is        larger than the second number;    -   step F. if the first number is smaller than the second number,        refusing the user to access the operating system of the handset;    -   step G. if the first number is not smaller than the second        number, allowing, by the cloud server, the user to access the        operating system of the handset.

In the embodiment of the present invention, the cloud server can bearthe load of identity authentication, thus improving the security of theoperating system of the handset, enhancing the user's experience andraising the precision of the face authentication.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart of a method for authenticating the identity of ahandset user according to the first embodiment of the present invention.

FIG. 2 is a schematic structural view illustrating a cloud serveraccording to the second embodiment of the present invention.

FIG. 3 is a schematic structural view illustrating a unit fordetermining a FFSV of the cloud server according to the secondembodiment of the present invention.

FIG. 4 is a schematic structural view illustrating a network systemaccording to the third embodiment of the present invention.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Cloud computing is an Internet-based computation method, by which sharedsoftware and hardware resources and information may be provided tocomputers, handsets and other devices as required. A typical cloudcomputing provider tends to provide general network serviceapplications, which may be accessed by software such as a browser orother Web services, and the software and data are all stored on a cloudserver.

Embodiments of the present invention are based on the cloud computingtechnology, and the task for authenticating the identity of a handsetmay be assigned to a cloud server, so that the burden of the handset isreduced, and high-overhead services can be introduced into the handset,thus enhancing the user's experience.

In the embodiments of the present invention, the user's handset isconnected to the cloud server through a communication network, the cloudserver stores a face sample image library corresponding to the user andmay be managed by a telecommunication operator, and the user registersthe face sample images with the cloud server of the telecommunicationoperator when subscribing. The cloud server binds the user's handsetnumber, the login account and password of the operating system of thehandset and other information to the face sample image library.

Embodiment 1

FIG. 1 shows a flowchart of a method for authenticating the identity ofa handset user according to the first embodiment of the presentinvention. As shown in FIG. 1, the method includes the following steps:

Step S101: The user enters a login account and a password on thehandset.

Step S103: The handset judges whether the login account and the passwordare correct.

Step S105: If the login account or the password is incorrect, the useris not allowed to access the operating system of the handset and anerror is displayed.

Step S107: If the login account and the password are correct, the loginaccount and the password are sent to the cloud server, where the loginaccount and the password correspond to a face sample image library ofthe user stored on the cloud server.

Step S109: The handset starts a camera to acquire an input face image ofthe user and send the input face image to the cloud server.

Step S111: The cloud server authenticates the identity of the useraccording to the login account, the password and the input face imageand judges whether the user is allowed to access the operating system ofthe handset. Step S111 specifically includes:

Step S111-2: The cloud server determines, according to the login accountand the password, the face sample image library of the usercorresponding to the login account and the password.

Step S111-4: The cloud server obtains a face feature similarity value(FFSV) according to the input face image and the face sample imagelibrary; the face feature similarity value represents the degree ofsimilarity between the input face image and each face sample image, andthe smaller the face feature similarity value (FFSV) is, the moresimilar the input face image and each face sample image are.

Step S111-4 specifically includes:

Step S111-4-1: Through face detection, the cloud server obtains a faceregion image from the face input image; mainly, the face detectionmethod is comparing the face skin color regions of the input face imageand the face sample images, and extracting the face region image basedon the proportion of the head shape.

Step S111-4-3: The cloud server calculates a first characteristic valueof each face sample image in the face sample image library and a secondcharacteristic value of the face region image.

Step S111-4-5: The cloud server calculates an characteristic distancebetween the first characteristic value of each face sample image in theface sample image library and the second characteristic value of theface region image, to obtain multiple second characteristic distances,and determines a face feature similarity value (FFSV) according to themultiple second characteristic distances; and the face featuresimilarity value (FFSV) represents the degree of similarity between theinput face image and each face sample image, and the smaller the facefeature similarity value is, the more similar the input face image andeach face sample image are. The face feature similarity value may be themaximum value among multiple second characteristic distances, or may bean average value of multiple second characteristic distances.

Step S111-6: The cloud server judges whether the face feature similarityvalue is larger than a preset threshold, wherein the preset threshold isobtained from multiple first characteristic distances between each facesample image in the face sample image library; and the preset thresholdmay be the maximum value in multiple first characteristic distances, ormay be an average value of multiple first characteristic distances.

Step S111-8: If the face feature similarity value is not larger than thepreset threshold, that is, the similarity of the user's face image andthe face sample images in the face sample image library meets securityrequirements, the user is allowed to access the operating system of thehandset.

Step S111-10: If the face feature similarity value is larger than thepreset threshold, that is, the similarity of the user's face image andthe face sample images in the face sample image library fails to meetsecurity requirements, the cloud server makes statistics respectivelyregarding how many face sample images whose first characteristicdistance is larger or smaller than the face feature similarity valuethere are, that is, a first number and a second number, wherein thefirst number is the number of face sample images in the face sampleimage library corresponding to the first characteristic distances whichare larger than the face feature similarity value, and the second numberis the number of face sample images in the face sample image librarycorresponding to the first characteristic distances which are not largerthan the face feature similarity value; and then, judges whether thefirst number is larger than the second number.

Step S111-12: If the first number is smaller than the second number, theuser is not allowed to access the operating system of the handset.

Step S111-14: If the first number is not smaller than the second number,the user is allowed to access the operating system of the handset.

The following further describes how the embodiment of the presentinvention extracts the face image features and determines the firstcharacteristic value of each face sample image in the face sample imagelibrary, the second characteristic value of the face region image, thefirst characteristic distances between the face sample images in theface sample image library, the second characteristic distance betweeneach face sample image in the face sample image library and the faceregion image, the preset threshold and the face feature similarityvalue.

A face sample image X(x,y) is taken as an example. The face sample imageX(x,y) is a 2-dimensional 64×64 grayscale image, where x represents ahorizontal coordinate pixel and y represents a vertical ordinate pixel.The face sample image library consists of M face sample images, and maybe represented by {X_(i)|i=1,2, . . . , M}. The M face sample images aresuperimposed according to face positions to obtain an average value ofall images after the superimposition, that is,

${\overset{\_}{X} = {\frac{1}{M}{\sum\limits_{i = 1}^{M}X_{i}}}},$

The difference between each face sample image X_(i) and the averagevalue X is: φ_(i)=X_(i)− X(i=1, . . . ,M).

A covariance matrix is constructed: C=AA^(T). Where, A=[φ₁, φ₂, . . . ,φ_(M)] is a linear combination of difference vectors. For a 64×64 faceimage, the size of the covariance matrix C is 4096×4096, and it isdifficult to obtain its eigenvalues and eigenvectors directly. Accordingto the singular value decomposition theorem, the eigenvalues and theeigenvectors of C=AA^(T) are obtained by finding the eigenvalues and theeigenvectors of A^(T) A. Assuming λ_(i)(i=1,2, . . . , r) is r nonzeroeigenvalues of the matrix A^(T) A, v_(i) and is an eigenvector of A^(T)A corresponding to λ_(i), then the orthogonal normalized eigenvector ofC=AA^(T) is

${u_{i} = {\frac{1}{\sqrt{\lambda_{i}}}A\; v_{i}}},$

and the eigenvalues corresponding to the sample covariance are arrangedin order of magnitude: λ₁≧λ₂≧. . . ≧λ_(r). Assuming that thecorresponding eigenvector of λ_(i) is u_(i), each face sample image maybe projected to an eigenspace U formed by u₁, u₂, . . . , u_(r).

In the specific application, the first d eigenvalues may be selected asthe eigenspace, and because the dimensions of this eigenspace are lowerthan the dimensions of the original face sample images, after each facesample image is projected to the eigenspace U formed by u₁, u₂, . . . ,u_(r), the dimensions of the face sample images are decreased greatly,thus achieving the objective of decreasing dimensions and extractingfeatures. The principle for selection is determined according to theenergy proportion of the eigenvalues α, and generally, the value of α isfrom 95% to 99%.

In order to improve the efficiency and precision of feature extraction,the embodiment of the present invention provides a method for findingthe eigenvectors of the face sample images by dividing the face sampleimages into blocks. A face has three distinguishing features: eyes, noseand mouth, and they are respectively located at upper, middle and lowerblocks of the face. Therefore, a face image is divided into threeindependent sub-blocks according to the three distinguishing features,that is, the upper part includes the eyes, the middle part includes thenose, and the lower part includes the mouth.

Through the block division, a face sample image becomes threesub-images, and thus each face sample image X_(i) may be denoted asX_(i)=[X_(i) ^(u) X_(i) ^(m) X_(i) ^(b)]^(T) (i=1,2, . . . , M), whereX_(i) ^(u) represents an upper sub-block image, X_(i) ^(m) representsthe middle sub-block image, and X_(i) ^(b) represents a lowersub-images.

The original one face sample image library becomes three sub-block imagelibraries independent of each other, that is, X_(i) ^(u), X_(i) ^(m) andX_(i) ^(b) (i=1,2, . . . ,M). If X_(i) is a matrix of P rows and Qcolumns, X_(i) ^(u) is a matrix of P₁ rows and Q columns, X_(i) ^(m) isa matrix of P₂ rows and Q columns, and X_(i) ^(b) is a matrix of P₃ rowsand Q columns, where P₁+P₂+P₃=P.

All upper sub-images of the face sample image library constitute anupper sub-block image library, and similarly, the middle and lowersub-images respectively constitute a middle sub-block image library anda lower sub-block image library. In the process of feature extraction,they will be treated as three independent sub-block image libraries.

Considering that the samples in the face sample image library arelimited, the embodiment of the present invention provides the followingalgorithm which can increase the number of samples without sampling,thus improving the precision of feature extraction. The methodspecifically includes the following steps:

1. For the face sample image X (an m×n matrix), a dual sample X′ isgenerated, where X′=XY, Y is an n×n matrix in which anti-diagonalelements are is and other elements are Os, that is:

${X = \begin{bmatrix}{X\left( {1,1} \right)} & {X\left( {1,2} \right)} & \ldots & {X\left( {1,n} \right)} \\\vdots & \vdots & \ddots & \vdots \\{X\left( {m,1} \right)} & {X\left( {m,2} \right)} & \ldots & {X\left( {m,n} \right)}\end{bmatrix}},{X^{\prime} = \begin{bmatrix}{X\left( {1,n} \right)} & {X\left( {1,{n - 1}} \right)} & \ldots & {X\left( {1,1} \right)} \\\vdots & \vdots & \ddots & \vdots \\{X\left( {m,n} \right)} & {X\left( {m,{n - 1}} \right)} & \ldots & {X\left( {m,1} \right)}\end{bmatrix}}$

Where, the matrix Y is symmetric, that is Y=Y^(T); and orthogonal, thatis YY^(T)=Y Y^(T)=I (I represents a unit matrix).

X is decomposed into a first sample X_(e)=(X+X′)/2 and a second sampleX_(o)=(X−X′)/2. Therefore, the relationship between the average value ofthe dual samples X′ and the covariance matrix C′ is as follows:

X′= XY, C′=Y ^(T) CY

The relationship between the average value of the first samples X_(e)and the covariance matrix C_(e) is as follows:

X _(e) = X (I+Y)/2, C _(e)=(I+Y)^(T) C(I+Y)/4

The relationship between the average value of the second samples X _(o)and the covariance matrix C_(o) is as follows:

X _(o) = X (I−Y)/2, C _(o)=(I−Y)^(T) C(I−Y)/4

According to mathematical theory, it can be determined that theeigenspace of the first samples X_(e) and the eigenspace of the secondsamples X _(o) are orthogonal, and that the eigenspace of X is a directsum of the eigenspace of the first samples X_(e) and the eigenspace ofthe second samples X _(o).

Therefore, the first eigenspace U_(e) and the second eigenspace U_(o)may be respectively obtained with respect to X_(e) and X_(o) based onthe feature extraction algorithm, and then, the eigenvectors with highrecognition precision and large variance are selected from the firsteigenspace U_(e) and the second eigenspace U_(o) to constitute theeigenspace U of X. Then, by using U as a feature transformation matrix,features are extracted through V=AU.

The method according to the embodiment of the present invention ishereinafter described in conjunction with the face sample image libraryafter block division. By taking the upper sub-block image library as anexample, a dual sample X_(i) ^(u)′(i=1,2, . . . ,M) is generated foreach sample X_(i) ^(u) (i=1,2, . . . ,M) in the upper sub-block imagelibrary, where X_(i) ^(u)′=X_(i) ^(u)Y, Y is a Q×Q matrix in which theanti-diagonal elements are is and other elements are 0s, that is:

$X_{i}^{u} = \begin{bmatrix}{X_{i}^{u}\left( {1,1} \right)} & {X_{i}^{u}\left( {1,2} \right)} & \ldots & {X_{i}^{u}\left( {1,Q} \right)} \\\vdots & \vdots & \ddots & \vdots \\{X_{i}^{u}\left( {P_{1},1} \right)} & {X_{i}^{u}\left( {P_{1},2} \right)} & \ldots & {X_{i}^{u}\left( {P_{1},Q} \right)}\end{bmatrix}$ ${X_{i}^{u}}^{\prime} = \begin{bmatrix}{X_{i}^{u}\left( {1,Q} \right)} & {X_{i}^{u}\left( {1,{Q - 1}} \right)} & \ldots & {X_{i}^{u}\left( {1,1} \right)} \\\vdots & \vdots & \ddots & \vdots \\{X_{i}^{u}\left( {P_{1},Q} \right)} & {X_{i}^{u}\left( {P,{Q - 1}} \right)} & \ldots & {X_{i}^{u}\left( {P,1} \right)}\end{bmatrix}$

X_(i) ^(u) is decomposed into a first sample X_(i e) ^(u)=(X_(i)^(u)+X_(i) ^(u)′)/2 and a second sample X_(i o) ^(u)=(X_(i) ^(u)−X_(i)^(u)′)/2.

A first eigenspace U^(u) _(i,e) and a second eigenspace U^(u) _(i,o) areobtained respectively with respect to X_(i e) ^(u) and X_(i o) ^(u)based on the foregoing feature extraction algorithm, and then, theeigenvectors with high recognition precision and large variance areselected from the first eigenspace U^(u) _(i,e) and the secondeigenspace U^(u) _(i,o) to constitute an eigenspace U^(u) _(i); and byusing U^(u) _(i) as a feature transformation matrix, the projection ofX_(i) ^(u) in the eigenspace U^(u) _(i), namely V_(i) ^(u), is extractedthrough V_(i) ^(u)=X_(i) ^(u)U^(u) _(i).

A feature extraction is performed with respect to each sample X_(i) ^(m)and X_(i) ^(b) (i=1,2, . . . ,M) in the middle sub-block image libraryand the lower sub-block image library by using the same method, wherethe projection of each sample X_(i) ^(m) and X_(i) ^(b) (i=1,2, . . .,M) of the middle sub-block image library and the lower sub-block imagelibrary in respective eigenspaces is recorded as V_(i) ^(m) and V_(i)^(b).

Assuming that V_(i) ^(u) is a k_(i,1)-dimensional vector, for aneigenmatrix

$V_{i}^{u} = \begin{bmatrix}{t^{i}\left( {1,1} \right)} & \ldots & {t^{i}\left( {1,k_{i,1}} \right)} \\\vdots & \ddots & \vdots \\{t^{i}\left( {P_{1},1} \right)} & \ldots & {t^{i}\left( {P_{1},k_{i,1}} \right)}\end{bmatrix}$

of each sample X_(i) ^(u) (i=1,2, . . . ,M) in the upper sub-block imagelibrary, a characteristic value T_(i) ^(u) is calculated respectively,that is:

$T_{i}^{u} = {\sqrt{\sum\limits_{{n = 1},{l = 1}}^{P_{1},k_{i,1}}\left( {t^{i}\left( {n,l} \right)} \right)^{2}}.}$

For the eigenspace V_(i) ^(m) (a k_(i,2)-dimensional vector) and V_(i)^(b) (a k_(i,3)-dimensional vector) of each sample X_(i) ^(m) and X_(i)^(b) (i=1,2, . . . ,M) of the middle sub-block image library and thelower sub-block image library, characteristic values

$T_{i}^{m} = {{\sqrt{\sum\limits_{{n = 1},{l = 1}}^{P_{2},k_{i,2}}\left( {t^{i}\left( {n,l} \right)} \right)^{2}}\mspace{14mu} {and}\mspace{14mu} T_{i}^{b}} = \sqrt{\sum\limits_{{n = 1},{l = 1}}^{P_{3},k_{i,3}}\left( {t^{i}\left( {n,l} \right)} \right)^{2}}}$

are calculated respectively.

An average value of the characteristic values T_(i) ^(u), T_(i) ^(m) andT_(i) ^(b) of each sample X_(i) ^(u), X_(i) ^(m) and X_(i) ^(b) of theupper sub-block image library, the middle sub-block image library andthe lower sub-block image library is calculated to obtain the firstcharacteristic value T_(i)=(T_(i) ^(u)+T_(i) ^(m)+T_(i) ^(b))/3 of eachface sample X_(i) in the face sample image library, where (i=1,2, . . .,M).

Described above is the processing of the face sample image library.Corresponding processing is performed for the input face region image byusing the same method, that is, dividing the input face region imageinto blocks, calculating the corresponding characteristic value of eachblock respectively, finding the sum of these characteristic values toobtain an average value, and finally obtaining the second characteristicvalue T of the input face region image.

The embodiment of the present invention provides a method forcalculating an characteristic distance, i.e. calculating multiple firstcharacteristic distances between face sample images according to a firstcharacteristic value of each face sample image in the face sample imagelibrary. The method specifically includes the following steps:

For face sample images X_(i) and X_(j) (i, j=1,2, . . . ,M, where i≠j),the first characteristic distance between the two face sample images isD(X_(i), X_(j))=√{square root over ((T_(i)−T_(j))²)}, and multiple firstcharacteristic distances between the face sample images are calculated.There are M*(M−1)/2 first characteristic distances in total.

Then, a preset threshold is obtained according to the M* (M−1)/2 firstcharacteristic distances between each face sample image in the facesample image library, where the preset threshold may be the maximumvalue in the M* (M−1)/2 first characteristic distances, or the averagevalue of the M* (M−1)/2 first characteristic distances.

Similarly, according to the second characteristic value T of the inputface region image and the first characteristic value of each face sampleimage in the face sample image library, multiple second characteristicvalues D(X_(i), X)=√{square root over ((T_(i)−T)²)} (i=1,2, . . . ,M)may be obtained, and there are M second characteristic distances.Then, a face feature similarity value is determined according to the Msecond characteristic distances, where the face feature similarity valuemay be the maximum value of the M second characteristic distances or theaverage value of the M second characteristic distances.

That is, the step of calculating the first characteristic value of eachface sample image in the face sample image library includes:

-   -   dividing the face sample image X_(i) into three sub-images, that        is, X_(i) ^(u), X_(i) ^(m) and X_(i) ^(b) (i=1,2, . . . ,M);    -   generating dual samples respectively for X_(i) ^(u), X_(i) ^(m)        and X_(i) ^(b);    -   decomposing X_(i) ^(u), X_(i) ^(m) and X_(i) ^(b) respectively        into a first sample X_(i e) ^(u)=(X_(i) ^(u)=X_(i) ^(u)′)/2 and        a second sample X_(i o) ^(u)=(X_(i) ^(u)−X_(i) ^(u)′)/2        according to the dual samples;    -   constructing a covariance matrix with respect to the first        sample and the second sample respectively;    -   determining the orthogonal normalized eigenvectors of the        covariance matrix of the first sample and the orthogonal        normalized eigenvectors of the covariance matrix of the second        sample respectively;    -   according to the first eigenspace formed by the orthogonal        normalized eigenvectors of the covariance matrix of the first        sample and the second eigenspace formed by the orthogonal        normalized eigenvectors of the covariance matrix of the second        sample, determining the projections of the first sample and the        second sample respectively in the first eigenspace and the        second eigenspace;    -   determining the characteristic values of X_(i) ^(u), X_(i) ^(m)        and X_(i) ^(b) according to the projections of the first sample        and the second sample in the first eigenspace and the second        eigenspace; and    -   determining the first characteristic value of the face sample        image X_(i) according to the characteristic values of X_(i)        ^(u), X_(i) ^(m) and X_(i) ^(b).

Calculating the second characteristic value of the input face regionimage includes the steps of:

-   -   dividing the input face region image into three sub-images;    -   generating corresponding dual samples respectively with respect        to the three sub-images;    -   decomposing the three sub-images into a first sample and a        second sample respectively according to the dual samples        corresponding to the three sub-images;    -   constructing a covariance matrix respectively with respect to        the first sample and the second sample of the three sub-images;    -   determining the orthogonal normalized eigenvectors of the        covariance matrix of the first sample and the orthogonal        normalized eigenvectors of the covariance matrix of the second        sample respectively;    -   according to the eigenspace formed by the orthogonal normalized        eigenvectors of the covariance matrix of the first sample and        the eigenspace formed by the orthogonal normalized eigenvectors        of the covariance matrix of the second sample, determining the        projections of the first sample and the second sample in the        eigenspace;    -   determining the characteristic values of the three sub-images        according to the projections of the first sample and the second        sample in the eigenspace; and    -   determining the second characteristic value of the input face        region image according to the characteristic values of the three        sub-images.

The embodiment of the present invention further includes: if the firstnumber is not smaller than the second number, updating the face sampleimage library by using the input face image, where the updating policymay be replacing the oldest face sample image, or replacing the facesample image that is most different from the input face image.

In addition, the first characteristic distance of the face sample imagelibrary in the cloud server may be recalculated, and according to thefirst characteristic distance, a new preset threshold may be determinedto replace the preset threshold. Thus, a dynamic update of the facesample image library is implemented.

With the method for authenticating the identity of a handset useraccording to the embodiment of the present invention, the cloud servercan bear the load of identity authentication, thus improving thesecurity of the operating system of the handset, enhancing the user'sexperience and raising the precision of the face authentication.

Embodiment 2

Another embodiment of the present invention further provides a cloudserver. As shown in FIG. 2, the cloud server includes:

-   -   a memory 200, configured to store a face sample image library of        a user;    -   a receiver 201, configured to receive a login account, a        password and an input face image of the user;    -   a determining unit 203, configured to determine the face sample        image library of the user corresponding to the login account and        the password; wherein the face sample image library of the user        is stored in the memory 200;    -   a unit for determining a face feature similarity value 205,        configured to determine a face feature similarity value (FFSV)        according to the input face image and the face sample image        library; as shown in FIG. 3, the unit for determining a FFSV 205        includes: a unit for obtaining a face region image 205-2, a unit        for calculating a characteristic value 205-4 and a unit for        calculating a characteristic distance 205-6, wherein:    -   the unit for obtaining a face region image 205-2, is configured        to obtains a face region image from the input face image through        face detection;    -   the unit for calculating a characteristic value 205-4, is        configured to calculate a first characteristic value of each        face sample image in the face sample image library and a second        characteristic value of the face region image; and    -   the unit for calculating a characteristic distance 205-6, is        configured to calculate an characteristic distance between the        first characteristic value of each face sample image in the face        sample image library and the second characteristic value of the        face region image, to obtain multiple second characteristic        distances, and determines a face feature similarity value (FFSV)        according to the multiple second characteristic distances;    -   a first determining unit 207, configured to judge whether the        face feature similarity value is larger than a preset threshold,        wherein the preset threshold is obtained from multiple first        characteristic distances between each face sample image in the        face sample image library;    -   a first permitting unit 209, configured to allow the user to        access the operating system of the handset, if the face feature        similarity value is not larger than the preset threshold;    -   a second determining unit 211, configured to determine a first        number and a second number, where the first number is the number        of face sample images in the face sample image library        corresponding to the first characteristic distances which are        larger than the face feature similarity value, and the second        number is the number of face sample images in the face sample        image library corresponding to the first characteristic        distances which are not larger than the face feature similarity        value, if the face feature similarity value is larger than the        preset threshold;    -   a refusing unit 213, configured to refuse the user to access the        operating system of the handset, if the first number is smaller        than the second number, a second permitting unit 215, configured        to allowed the user to access the operating system of the        handset, if the first number is not smaller than the second        number.

Optionally, the cloud server may further includes: a first updating unit217, configured to update the face sample image library by using theinput face image, if the first number is not smaller than the secondnumber.

Optionally, the cloud server may further includes: a second updatingunit 219, configured to recalculate the first characteristic distance ofthe face sample image library in the cloud server, and according to thefirst characteristic distance, replace the preset threshold with the newpreset threshold.

-   -   the unit for calculating a characteristic value 205-4 includes:    -   a first dividing unit 205-41, configured to divide the face        sample image X_(i) into three sub-images, that is and is, X_(i)        ^(u), X_(i) ^(m) and X_(i) ^(b)(i=1,2, . . . , M);    -   a first generating unit 205-43, configured to generate dual        samples respectively for X_(i) ^(u), X_(i) ^(m) and X_(i) ^(b);    -   a first decomposing unit 205-45, configured to decompose X_(i)        ^(u), X_(i) ^(m) and X_(i) ^(b) respectively into a first sample        X_(i e) ^(u)=(X_(i) ^(u)+X_(i) ^(u)′)/2 and a second sample        X_(i o) ^(u)=(X_(i) ^(u)−X_(i) ^(u)′)/2 according to the dual        samples;    -   a first covariance matrix constructing unit 205-47, configured        to construct a covariance matrix with respect to the first        sample and the second sample respectively;    -   a first eigenvector calculating unit 205-49, configured to        determine the orthogonal normalized eigenvectors of the        covariance matrix of the first sample and the orthogonal        normalized eigenvectors of the covariance matrix of the second        sample respectively;    -   a first projection calculating unit 205-411, configured to        determine the projections of the first sample and the second        sample respectively in the first eigenspace and the second        eigenspace, according to the first eigenspace formed by the        orthogonal normalized eigenvectors of the covariance matrix of        the first sample and the second eigenspace formed by the        orthogonal normalized eigenvectors of the covariance matrix of        the second sample;    -   a first characteristic value calculating unit 205-413,        configured to determine the characteristic values of X_(i) ^(u),        X_(i) ^(m) and X_(i) ^(b) according to the projections of the        first sample and the second sample in the first eigenspace and        the second eigenspace;    -   a second dividing unit 205-415, configured to divide the input        face region image into three sub-images;    -   a second generating unit 205-417, configured to generate        corresponding dual samples respectively with respect to the        three sub-images;    -   a second decomposing unit 205-419, configured to decompose three        sub-images into a first sample and a second sample according to        the dual samples corresponding to the three sub-images;    -   a second covariance matrix constructing unit 205-421, configured        to construct a covariance matrix respectively with respect to        the first sample and the second sample of the three sub-images;    -   a second eigenvector calculating unit 205-423, configured to        determine the orthogonal normalized eigenvectors of the        covariance matrix of the first sample and the orthogonal        normalized eigenvectors of the covariance matrix of the second        sample respectively;    -   a second projection calculating unit 205-425, configured to        determine the projections of the first sample and the second        sample respectively in the eigenspace, according to the first        eigenspace formed by the orthogonal normalized eigenvectors of        the covariance matrix of the first sample and the second        eigenspace formed by the orthogonal normalized eigenvectors of        the covariance matrix of the second sample;    -   a second characteristic value calculating unit 205-427,        configured to determine characteristic values of the three        sub-images according to the projections of the first sample and        the second sample in the eigenspace, and the characteristic        values of the input face region according to the characteristic        values of the three sub-images.

In the embodiment of the present invention, the cloud server can bearthe load of identity authentication, thus improving the security of theoperating system of the handset, enhancing the user's experience andraising the precision of the face authentication.

Embodiment 3

One embodiment of the present invention further provide a network systemas shown in FIG. 4, including a handset 502 and a cloud server 504,wherein the user's handset 502 is connected to the cloud server 504through a communication network; and the handset is configured to obtaina login account and a password input by the user, judge whether thelogin account and the password are correct; if the login account or thepassword is incorrect, allow the user to access the operating system ofthe handset; if the login account and the password are correct, send thelogin account and the password to the cloud server, where the loginaccount and the password correspond to a face sample image library ofthe user stored on the cloud server; acquire an input face image of theuser and send the input face image to the cloud server;

-   -   the cloud server is configured to store a face sample image        library corresponding to the user, authenticate the identity of        the user according to the login account, the password and the        input face image and judge whether the user is allowed to access        the operating system of the handset.

The function specially includes:

Step A. The cloud server determines, according to the login account andthe password, the face sample image library of the user corresponding tothe login account and the password;

Step B. The cloud server obtains a face feature similarity value (FFSV)according to the face input image and the face sample image library; andthe step B especially includes:

B1.Through face detection, the cloud server obtains a face region imagefrom the face input image;

B2. The cloud server calculates a first characteristic value of eachface sample image in the face sample image library and a secondcharacteristic value of the face region image

B3. The cloud server calculates an characteristic distance between thefirst characteristic value of each face sample image in the face sampleimage library and the second characteristic value of the face regionimage, to obtain multiple second characteristic distances, anddetermines a face feature similarity value (FFSV) according to themultiple second characteristic distances;

Step C. The cloud server judges whether the face feature similarityvalue is larger than a preset threshold, where the preset threshold isobtained from multiple first characteristic distances between each facesample image in the face sample image library;

Step D. If the face feature similarity value is not larger than thepreset threshold, that is, the similarity of the user's face image andthe face sample images in the face sample image library meets securityrequirements, the user is allowed to access the operating system of thehandset;

Step E. If the face feature similarity value is larger than the presetthreshold, that is, the similarity of the user's face image and the facesample images in the face sample image library fails to meet securityrequirements, the cloud server makes statistics respectively regardinghow many face sample images whose first characteristic distance islarger or smaller than the face feature similarity value there are, thatis, a first number and a second number, where the first number is thenumber of face sample images in the face sample image librarycorresponding to the first characteristic distances which are largerthan the face feature similarity value, and the second number is thenumber of face sample images in the face sample image librarycorresponding to the first characteristic distances which are not largerthan the face feature similarity value; and then, judges whether thefirst number is larger than the second number;

Step F. If the first number is smaller than the second number, the useris not allowed to access the operating system of the handset;

Step G. If the first number is not smaller than the second number, theuser is allowed to access the operating system of the handset.

The structure of the cloud server can be the one as described inEmbodiment 2.

In the embodiment of the present invention, the cloud server can bearthe load of identity authentication, thus improving the security of theoperating system of the handset, enhancing the user's experience andraising the precision of the face authentication.

Persons of ordinary skill in the art should understand that all or apart of the steps of the method according to the embodiments of thepresent invention may be implemented by a program instructing relevanthardware. The program may be stored in a computer readable storagemedium. When the program is run, the steps of the method according tothe embodiments of the present invention are performed. The storagemedium may be any medium that is capable of storing program codes, suchas a Read Only Memory (ROM), a Random Access Memory (RAM), a magneticdisk, and an optical disk.

Finally, it should be noted that the above embodiments are merelyprovided for describing the technical solutions of the presentinvention, but not intended to limit the present invention. It should beunderstood by persons of ordinary skill in the art that although thepresent invention has been described in detail with reference to theforegoing embodiments, modifications can be made to the technicalsolutions described in the foregoing embodiments, or equivalentreplacements can be made to some technical features in the technicalsolutions, as long as such modifications or replacements do not causethe essence of corresponding technical solutions to depart from thespirit and scope of the present invention.

1. A method for authenticating the identity of a handset user, whereinthe handset is connected to a cloud server through a communicationnetwork, the cloud server stores a face sample image librarycorresponding to the user, and the method comprises: obtaining, by thehandset, a login account and a password from the user; judging, by thehandset, whether the login account and the password are correct; if thelogin account or the password is incorrect, refusing, by the handset,the user to access an operating system of the handset; if the loginaccount and the password are correct, sending, by the handset, the loginaccount and the password to the cloud server, wherein the login accountand the password correspond to a face sample image library of the userstored on the cloud server; acquiring, by the handset, an input faceimage of the user; sending, by the handset, the input face image to thecloud server; authenticating, by the cloud server, the identity of theuser according to the login account, the password and the input faceimage; judging, by the cloud server, whether the user is allowed toaccess the operating system of the handset; wherein the step of judgingwhether the user is allowed to access the operating system of thehandset comprises: step A. determining, by the cloud server, accordingto the login account and the password, the face sample image library ofthe user corresponding to the login account and the password; step B.obtaining, by the cloud server, a face feature similarity value, FFSV,according to the input face image and the face sample image library;wherein the face feature similarity value represents the degree ofsimilarity between the input face image and each face sample image inthe face sample image library; step C. judging, the cloud server,whether the face feature similarity value is larger than a presetthreshold, wherein the preset threshold is obtained according tomultiple first characteristic distances between each face sample imagein the face sample image library; step D. if the face feature similarityvalue is not larger than the preset threshold, allowing, by the cloudserver, the user to access the operating system of the handset; step E.if the face feature similarity value is larger than the presetthreshold, calculating, by the cloud server, a first number and a secondnumber, wherein the first number is the number of face sample images inthe face sample image library corresponding to the first characteristicdistances which are larger than the face feature similarity value, andthe second number is the number of face sample images in the face sampleimage library corresponding to the first characteristic distances whichare not larger than the face feature similarity value; judging, by thecloud server, whether the first number is larger than the second number;step F. if the first number is smaller than the second number, refusingthe user to access the operating system of the handset; step G. if thefirst number is not smaller than the second number, allowing, by thecloud server, the user to access the operating system of the handset;and the step of B comprises: step B1. obtaining, by the cloud server, aface region image from the input face image; step B2. calculating, bythe cloud server, a first characteristic value of each face sample imagein the face sample image library and a second characteristic value ofthe face region image; step B3. calculating, by the cloud server, acharacteristic distance between the first characteristic value of eachface sample image in the face sample image library and the secondcharacteristic value of the face region image, to obtain multiple secondcharacteristic distances; determining, by the cloud server, the facefeature similarity value, FFSV, according to the multiple secondcharacteristic distances.
 2. The method according to claim 1, whereinthe step of calculating a first characteristic value of each face sampleimage in the face sample image library comprises: dividing the facesample image X_(i) into three sub-images X_(i) ^(u), X_(i) ^(m) andX_(i) ^(b) (i=1,2, . . . ,M); generating dual samples respectively forX_(i) ^(u), X_(i) ^(m) and X_(i) ^(b); decomposing X_(i) ^(u), X_(i)^(m) and X_(i) ^(b) respectively into a first sample X_(i e) ^(u)=(X_(i)^(u)+X_(i) ^(u)′)/2 and a second sample X_(i o) ^(u)=(X_(i) ^(u)−X_(i)^(u)′)/2 according to the dual samples; constructing a covariance matrixwith respect to the first sample and the second sample respectively;determining the orthogonal normalized eigenvectors of the covariancematrix of the first sample and the orthogonal normalized eigenvectors ofthe covariance matrix of the second sample respectively; according tothe first eigenspace formed by the orthogonal normalized eigenvectors ofthe covariance matrix of the first sample and the second eigenspaceformed by the orthogonal normalized eigenvectors of the covariancematrix of the second sample, determining the projections of the firstsample and the second sample respectively in the first eigenspace andthe second eigenspace; determining the characteristic values of X_(i)^(u), X_(i) ^(m) and X_(i) ^(b) according to the projections of thefirst sample and the second sample in the first eigenspace and thesecond eigenspace; and determining the first characteristic value of theface sample image X_(i) according to the characteristic values of X_(i)^(u), X_(i) ^(m) and X_(i) ^(b); and the step of calculating the secondcharacteristic value of the input face region image comprises: dividingthe input face region image into three sub-images; generatingcorresponding dual samples respectively with respect to the threesub-images; decomposing the three sub-images into a first sample and asecond sample respectively according to the dual samples correspondingto the three sub-images; constructing a covariance matrix respectivelywith respect to the first sample and the second sample of the threesub-images; determining the orthogonal normalized eigenvectors of thecovariance matrix of the first sample and the orthogonal normalizedeigenvectors of the covariance matrix of the second sample respectively;according to the eigenspace formed by the orthogonal normalizedeigenvectors of the covariance matrix of the first sample and theeigenspace formed by the orthogonal normalized eigenvectors of thecovariance matrix of the second sample, determining the projections ofthe first sample and the second sample in the eigenspace; determiningthe characteristic values of the three sub-images according to theprojections of the first sample and the second sample in the eigenspace;and determining the second characteristic value of the input face regionimage according to the characteristic values of the three sub-images.3-12. (canceled)